The rapid development of reliable quantum processing units opens up novel computational opportunities for machine learning. Here, we introduce a procedure for measuring the similarity between graph-structured data, based on the time evolution of a quantum system. By encoding the topology of the input graph in the Hamiltonian of the system, the evolution produces measurement samples that retain key features of the data. We study analytically the procedure and illustrate its versatility in providing links to standard classical approaches. We then show numerically that this scheme performs well compared to standard graph kernels on typical benchmark datasets. Finally, we study the possibility of a concrete implementation on a realistic neutral-atom quantum processor.

中文翻译：

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量子进化内核：具有可编程量子位阵列的图上的机器学习

可靠的量子处理单元的快速发展为机器学习开辟了新的计算机会。在这里，我们介绍了一种基于量子系统的时间演化来测量图结构数据之间相似性的过程。通过在系统的哈密顿量中对输入图的拓扑结构进行编码，演化产生保留数据关键特征的测量样本。我们分析地研究了该程序，并说明了它在提供与标准经典方法的链接方面的多功能性。然后，我们以数字方式表明，与典型基准数据集上的标准图内核相比，该方案表现良好。最后，我们研究了在现实中性原子量子处理器上具体实现的可能性。